Improvement of the current double inverted pendulum model on a cart as well as a new LQG control and swing-up realization are the main goal of this thesis. Movement of the cart is driven by DC motor and gear belt mechanism. At first the control algorithms were simulated in Simulink program and then also implemented into the real system with MF624 card.

Les modélisations par chaînes de Markov cachées permettent de résoudre un grand nombre de problèmes inverses se posant en traitement d’images ou de signaux. En particulier, le problème de segmentation figure parmi les problèmes où ces modèles ont été le plus sollicités. Selon ces modèles, la donnée observable est considérée comme une version bruitée de la segmentation recherchée qui peut être modélisée à travers une chaîne de Markov à états finis. Des techniques bayésiennes permettent ensuite d’estimer cette segmentation même dans le contexte non-supervisé grâce à des algorithmes qui permettent d’estimer les paramètres du modèle à partir de l’observation seule. Les chaînes de Markov cachées ont été ultérieurement généralisées aux chaînes de Markov couples et triplets, lesquelles offrent plus de possibilités de modélisation tout en présentant des complexités de calcul comparables, permettant ainsi de relever certains défis que les modélisations classiques ne supportent pas. Un lien intéressant a également été établi entre les modèles de Markov triplets et la théorie de l’évidence de Dempster-Shafer, ce qui confère à ces modèles la possibilité de mieux modéliser les données multi-senseurs. Ainsi, dans cette thèse, nous abordons trois difficultés qui posent problèmes aux modèles classiques : la non-stationnarité du processus caché et/ou du bruit, la corrélation du bruit et la multitude de sources de données. Dans ce cadre, nous proposons des modélisations originales fondées sur la très riche théorie des chaînes de Markov triplets. Dans un premier temps, nous introduisons les chaînes de Markov à bruit M-stationnaires qui tiennent compte de l’aspect hétérogène des distributions de bruit s’inspirant des chaînes de Markov cachées M-stationnaires. Les chaînes de Markov cachée ML-stationnaires, quant à elles, considèrent à la fois la loi a priori et les densités de bruit non-stationnaires. Dans un second temps, nous définissons deux types de chaînes de Markov couples non-stationnaires. Dans le cadre bayésien, nous introduisons les chaînes de Markov couples M-stationnaires puis les chaînes de Markov couples MM-stationnaires qui considèrent la donnée stationnaire par morceau. Dans le cadre évidentiel, nous définissons les chaînes de Markov couples évidentielles modélisant l’hétérogénéité du processus caché par une fonction de masse. Enfin, nous présentons les chaînes de Markov multi-senseurs non-stationnaires où la fusion de Dempster-Shafer est employée à la fois pour modéliser la non-stationnarité des données (à l’instar des chaînes de Markov évidentielles cachées) et pour fusionner les informations provenant des différents senseurs (comme dans les champs de Markov multi-senseurs). Pour chacune des modélisations proposées, nous décrivons les techniques de segmentation et d’estimation des paramètres associées. L’intérêt de chacune des modélisations par rapport aux modélisations classiques est ensuite démontré à travers des expériences menées sur des données synthétiques et réelles / Hidden Markov chains (HMCs) based approaches have been shown to be efficient to resolve a wide range of inverse problems occurring in image and signal processing. In particular, unsupervised segmentation of data is one of these problems where HMCs have been extensively applied. According to such models, the observed data are considered as a noised version of the requested segmentation that can be modeled through a finite Markov chain. Then, Bayesian techniques such as MPM can be applied to estimate this segmentation even in unsupervised way thanks to some algorithms that make it possible to estimate the model parameters from the only observed data. HMCs have then been generalized to pairwise Markov chains (PMCs) and triplet Markov chains (TMCs), which offer more modeling possibilities while showing comparable computational complexities, and thus, allow to consider some challenging situations that the conventional HMCs cannot support. An interesting link has also been established between the Dempster-Shafer theory of evidence and TMCs, which give to these latter the ability to handle multisensor data. Hence, in this thesis, we deal with three challenging difficulties that conventional HMCs cannot handle: nonstationarity of the a priori and/or noise distributions, noise correlation, multisensor information fusion. For this purpose, we propose some original models in accordance with the rich theory of TMCs. First, we introduce the M-stationary noise- HMC (also called jumping noise- HMC) that takes into account the nonstationary aspect of the noise distributions in an analogous manner with the switching-HMCs. Afterward, ML-stationary HMC consider nonstationarity of both the a priori and/or noise distributions. Second, we tackle the problem of non-stationary PMCs in two ways. In the Bayesian context, we define the M-stationary PMC and the MM-stationary PMC (also called switching PMCs) that partition the data into M stationary segments. In the evidential context, we propose the evidential PMC in which the realization of the hidden process is modeled through a mass function. Finally, we introduce the multisensor nonstationary HMCs in which the Dempster-Shafer fusion has been used on one hand, to model the data nonstationarity (as done in the hidden evidential Markov chains) and on the other hand, to fuse the information provided by the different sensors (as in the multisensor hidden Markov fields context). For each of the proposed models, we describe the associated segmentation and parameters estimation procedures. The interest of each model is also assessed, with respect to the former ones, through experiments conducted on synthetic and real data

Voltage instability is a major concern in operation of power systems and it is well known that voltage instability and collapse have led to blackout or abnormally low voltages in a significant part of the power system. Consequently, tracking the proximity of the power system to an insecure voltage condition has become an important element of any protection and control scheme. The expected time until instability is a critical aspect. There are a few energy management systems including voltage stability analysis function in the real-time environment of control centres, these are based on assumptions (such as off-line models of the system loads) that may lead the system to an insecure operation and/or poor utilization of the resources. Voltage instability is driven by the load dynamics, and investigations have shown that load restoration due to the on-load tap changer (OLTC) action is the main cause of the voltage instability. However, the aggregate loads seen from bulk power delivery transformers are still the most uncertain power system components, due to the uncertainty of the participation of individual loads and shortcomings of the present approaches in the load modeling. In order to develop and implement a true on-line voltage stability analysis method, the on-line accurate modeling of the higher voltage (supply system) and the lower voltage level (aggregate load) based on the local measurements is required. In this research, using the changes in the load bus measured voltage and current, novel methods are developed to estimate the supply system equivalent and to identify load parameters. Random changes in the load voltage and current are processed to estimate the supply system Thevenin impedance and the composite load components are identified in a peeling process using the load bus data changes during a large disturbance in the system. The results are then used to anticipate a possible long-term voltage instability caused by the on-load tap changer operation following the disturbance. Work on the standard test system is provided to validate the proposed methods. The findings in this research are expected to provide a better understanding of the load dynamics role in the voltage stability, and improve the reliability and economy of the system operation by making it possible to decrease uncertainty in security margins and determine accurately the transfer limits.

<p>Additive manufacturing of gun propellants is an emerging and promising field which addresses the limitations of conventional manufacturing techniques. Gun propellants are manufactured using wetted extrusion, which uses volatile solvents and dies of limited and constant geometries. On the other hand, additive techniques are faced with the challenges of maintaining the gun propellant’s energetic content as well as its structural integrity during high pressure combustion. The work presented in this thesis demonstrates the feasibility of producing functioning gun propellant grains using vibration-assisted 3D printing, a novel method which has been shown to extrude extremely viscous materials such as clays and propellant pastes. At first, the technique is compared to screw-driven additive methods which have been used in printing gun propellant pastes with slightly lower energetic content. In chapter two, diethylene glycol dinitrate (DEGDN), a highly energetic plasticizer, was investigated due to its potential to replace nitroglycerin in double base propellants with high nitroglycerin content. A novel isoconversional method was applied to analyze its decomposition kinetics. The ignition and lifetime values of diethylene glycol dinitrate were obtained using the new isoconversional method, in order to assess the safety of using the plasticizer in a modified double base propellant. In chapter three, a modified double base propellant (M8D) containing DEGDN was additively manufactured using VAP. The printed strands had little to no porosity, and their density was nearly equal to the theoretical maximum density of the mixture. The strands were burned at high pressures in a Crawford bomb and the burning was visualized using high speed cameras. The burning rate equation as a function of the M8D propellant as a function of pressure was obtained. Overall, this work shows that VAP is capable of printing highly energetic gun propellants with low solvent content, low porosity, with high printing speeds, and which have consistent burning characteristics at high pressures. </p>